Web Scale on a Budget: Tail-Aware Retrieval Evaluation
A common practice in instance retrieval is to grow the gallery size to improve a benchmark's discrimination capability. One prominent example is ILIAS, which adds up to 100 million distractor images. The prevailing assumption is that faithful evaluation demands web scale, and that indexing all 100M images through the backbone is the unavoidable price of a comparable number. That price is steep: hundreds of GPU-hours and ~200 GB of descriptors per model, paid again for every method or ablation. We show the web-scale indexing price is largely avoidable: evaluation itself obeys a scaling law. The number of distractors that outrank a positive is Binomial in the gallery size. We define the per...
| Authors | Boaz Meivar, Shai Avidan |
|---|---|
| Primary category | cs.CV |
| Published | 13 October 2026 |
| Last updated at source | 13 October 2026 |
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